Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations5000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory657.8 KiB
Average record size in memory134.7 B

Variable types

Numeric9
Categorical1

Alerts

PM10 is highly overall correlated with PM2.5High correlation
PM2.5 is highly overall correlated with PM10High correlation
SO2 has 169 (3.4%) zeros Zeros
Proximity_to_Industrial_Areas has 60 (1.2%) zeros Zeros

Reproduction

Analysis started2024-12-02 21:35:18.978243
Analysis finished2024-12-02 21:35:25.192385
Duration6.21 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Temperature
Real number (ℝ)

Distinct331
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.46458
Minimum3.5
Maximum46.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-12-02T22:35:25.262701image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile16.9
Q121.8
median25.3
Q328.9
95-th percentile34.7
Maximum46.2
Range42.7
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation5.486219
Coefficient of variation (CV)0.2154451
Kurtosis0.60280894
Mean25.46458
Median Absolute Deviation (MAD)3.5
Skewness0.23391162
Sum127322.9
Variance30.098599
MonotonicityNot monotonic
2024-12-02T22:35:25.363791image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.8 50
 
1.0%
26.7 48
 
1.0%
28 47
 
0.9%
22.7 47
 
0.9%
24.9 47
 
0.9%
25.4 47
 
0.9%
26.4 46
 
0.9%
24.8 46
 
0.9%
23.1 46
 
0.9%
25.5 46
 
0.9%
Other values (321) 4530
90.6%
ValueCountFrequency (%)
3.5 1
< 0.1%
5.2 1
< 0.1%
5.3 1
< 0.1%
6.1 2
< 0.1%
7.5 1
< 0.1%
8.3 1
< 0.1%
8.5 1
< 0.1%
8.6 2
< 0.1%
9.2 1
< 0.1%
9.7 1
< 0.1%
ValueCountFrequency (%)
46.2 1
 
< 0.1%
45.9 1
 
< 0.1%
45.8 2
< 0.1%
45.4 3
0.1%
45.2 1
 
< 0.1%
44.9 1
 
< 0.1%
44.7 2
< 0.1%
44.6 1
 
< 0.1%
44.2 2
< 0.1%
44.1 1
 
< 0.1%

Humidity
Real number (ℝ)

Distinct724
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.06814
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-12-02T22:35:25.461334image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile34.9
Q149.9
median60.2
Q370.1
95-th percentile84.9
Maximum100
Range90
Interquartile range (IQR)20.2

Descriptive statistics

Standard deviation15.044806
Coefficient of variation (CV)0.25046233
Kurtosis-0.090625585
Mean60.06814
Median Absolute Deviation (MAD)10.1
Skewness-0.039819814
Sum300340.7
Variance226.3462
MonotonicityNot monotonic
2024-12-02T22:35:25.563123image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.3 23
 
0.5%
61 20
 
0.4%
57.3 20
 
0.4%
60.2 20
 
0.4%
61.6 20
 
0.4%
64.6 20
 
0.4%
100 19
 
0.4%
62.4 19
 
0.4%
66.2 19
 
0.4%
63.4 19
 
0.4%
Other values (714) 4801
96.0%
ValueCountFrequency (%)
10 4
0.1%
13 1
 
< 0.1%
14.4 1
 
< 0.1%
15.4 1
 
< 0.1%
15.5 2
< 0.1%
16 1
 
< 0.1%
16.4 1
 
< 0.1%
17.4 1
 
< 0.1%
17.6 1
 
< 0.1%
17.9 1
 
< 0.1%
ValueCountFrequency (%)
100 19
0.4%
99.8 2
 
< 0.1%
99.7 1
 
< 0.1%
99.4 1
 
< 0.1%
99.3 1
 
< 0.1%
99.2 2
 
< 0.1%
98.9 1
 
< 0.1%
98.6 1
 
< 0.1%
98.4 2
 
< 0.1%
97.9 2
 
< 0.1%

PM2.5
Real number (ℝ)

High correlation 

Distinct1008
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.90558
Minimum0
Maximum249
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-12-02T22:35:25.661095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q18.5
median20.6
Q341.5
95-th percentile91.205
Maximum249
Range249
Interquartile range (IQR)33

Descriptive statistics

Standard deviation30.285899
Coefficient of variation (CV)1.0127173
Kurtosis5.8785757
Mean29.90558
Median Absolute Deviation (MAD)14.3
Skewness2.0250291
Sum149527.9
Variance917.23568
MonotonicityNot monotonic
2024-12-02T22:35:25.764379image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 23
 
0.5%
2 21
 
0.4%
12.7 21
 
0.4%
8.4 20
 
0.4%
3.5 20
 
0.4%
2.6 20
 
0.4%
5.3 20
 
0.4%
1.3 20
 
0.4%
7.6 20
 
0.4%
1.9 20
 
0.4%
Other values (998) 4795
95.9%
ValueCountFrequency (%)
0 9
0.2%
0.1 12
0.2%
0.2 15
0.3%
0.3 13
0.3%
0.4 19
0.4%
0.5 17
0.3%
0.6 19
0.4%
0.7 16
0.3%
0.8 19
0.4%
0.9 12
0.2%
ValueCountFrequency (%)
249 1
< 0.1%
241.6 1
< 0.1%
223 1
< 0.1%
214.4 1
< 0.1%
214.3 1
< 0.1%
213.7 1
< 0.1%
212.8 1
< 0.1%
209.2 1
< 0.1%
204.3 1
< 0.1%
199.6 1
< 0.1%

PM10
Real number (ℝ)

High correlation 

Distinct1098
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.0037
Minimum-1.4
Maximum256.1
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)0.1%
Memory size39.2 KiB
2024-12-02T22:35:25.866126image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-1.4
5-th percentile9.195
Q118.9
median31.1
Q351.5
95-th percentile103.4
Maximum256.1
Range257.5
Interquartile range (IQR)32.6

Descriptive statistics

Standard deviation30.693124
Coefficient of variation (CV)0.76725712
Kurtosis5.4487277
Mean40.0037
Median Absolute Deviation (MAD)14.4
Skewness1.9381335
Sum200018.5
Variance942.06785
MonotonicityNot monotonic
2024-12-02T22:35:25.972718image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.4 21
 
0.4%
14.3 21
 
0.4%
22 20
 
0.4%
21.7 20
 
0.4%
18.2 18
 
0.4%
17.8 18
 
0.4%
21.3 18
 
0.4%
18.3 18
 
0.4%
21.1 18
 
0.4%
14.5 16
 
0.3%
Other values (1088) 4812
96.2%
ValueCountFrequency (%)
-1.4 1
 
< 0.1%
-1 1
 
< 0.1%
-0.7 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
1 1
 
< 0.1%
1.5 1
 
< 0.1%
1.7 1
 
< 0.1%
1.8 3
0.1%
ValueCountFrequency (%)
256.1 1
< 0.1%
239 1
< 0.1%
234 1
< 0.1%
228.5 1
< 0.1%
223.8 1
< 0.1%
223.2 1
< 0.1%
222.2 1
< 0.1%
219.3 1
< 0.1%
217 1
< 0.1%
214.6 1
< 0.1%

NO2
Real number (ℝ)

Distinct598
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.00036
Minimum-13.5
Maximum96.4
Zeros1
Zeros (%)< 0.1%
Negative102
Negative (%)2.0%
Memory size39.2 KiB
2024-12-02T22:35:26.075141image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-13.5
5-th percentile3.9
Q113.8
median20.5
Q327.5
95-th percentile38.805
Maximum96.4
Range109.9
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation11.30099
Coefficient of variation (CV)0.53813315
Kurtosis2.5606544
Mean21.00036
Median Absolute Deviation (MAD)6.8
Skewness0.7506528
Sum105001.8
Variance127.71237
MonotonicityNot monotonic
2024-12-02T22:35:26.174436image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.4 30
 
0.6%
18.3 26
 
0.5%
14.6 26
 
0.5%
23.9 25
 
0.5%
15.7 25
 
0.5%
20.6 24
 
0.5%
21.2 24
 
0.5%
22.1 24
 
0.5%
23.6 24
 
0.5%
21.4 24
 
0.5%
Other values (588) 4748
95.0%
ValueCountFrequency (%)
-13.5 1
< 0.1%
-11.7 1
< 0.1%
-11.5 1
< 0.1%
-11.3 1
< 0.1%
-10.4 1
< 0.1%
-9.4 1
< 0.1%
-9.1 1
< 0.1%
-9 1
< 0.1%
-8.5 1
< 0.1%
-8.2 1
< 0.1%
ValueCountFrequency (%)
96.4 1
< 0.1%
88 1
< 0.1%
86.1 1
< 0.1%
81.8 1
< 0.1%
80.3 1
< 0.1%
79.4 1
< 0.1%
77.5 1
< 0.1%
76.7 1
< 0.1%
75.9 1
< 0.1%
75.7 1
< 0.1%

SO2
Real number (ℝ)

Zeros 

Distinct360
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.14106
Minimum0
Maximum41.7
Zeros169
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-12-02T22:35:26.274547image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6
Q19.9
median15.1
Q320.4
95-th percentile27.7
Maximum41.7
Range41.7
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation7.6684659
Coefficient of variation (CV)0.50646823
Kurtosis-0.27597557
Mean15.14106
Median Absolute Deviation (MAD)5.3
Skewness0.094924674
Sum75705.3
Variance58.805369
MonotonicityNot monotonic
2024-12-02T22:35:26.585049image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
 
3.4%
14 35
 
0.7%
16.2 33
 
0.7%
11.5 32
 
0.6%
11.1 31
 
0.6%
11.6 31
 
0.6%
16.6 31
 
0.6%
14.9 31
 
0.6%
12.2 31
 
0.6%
14.3 31
 
0.6%
Other values (350) 4545
90.9%
ValueCountFrequency (%)
0 169
3.4%
0.1 1
 
< 0.1%
0.2 7
 
0.1%
0.3 5
 
0.1%
0.4 4
 
0.1%
0.5 5
 
0.1%
0.6 5
 
0.1%
0.7 4
 
0.1%
0.8 6
 
0.1%
0.9 7
 
0.1%
ValueCountFrequency (%)
41.7 1
 
< 0.1%
41 1
 
< 0.1%
39.8 1
 
< 0.1%
39.3 1
 
< 0.1%
38.9 1
 
< 0.1%
38.6 1
 
< 0.1%
38.4 3
0.1%
38.3 1
 
< 0.1%
38.2 1
 
< 0.1%
38 1
 
< 0.1%

CO
Real number (ℝ)

Distinct186
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.904314
Minimum-0.08
Maximum2.14
Zeros1
Zeros (%)< 0.1%
Negative8
Negative (%)0.2%
Memory size39.2 KiB
2024-12-02T22:35:26.687162image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-0.08
5-th percentile0.42
Q10.7
median0.905
Q31.1
95-th percentile1.39
Maximum2.14
Range2.22
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.29784014
Coefficient of variation (CV)0.32935479
Kurtosis0.019793885
Mean0.904314
Median Absolute Deviation (MAD)0.205
Skewness-0.011361633
Sum4521.57
Variance0.088708751
MonotonicityNot monotonic
2024-12-02T22:35:26.791131image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81 86
 
1.7%
0.88 79
 
1.6%
1.09 79
 
1.6%
0.84 76
 
1.5%
1.01 75
 
1.5%
0.93 75
 
1.5%
0.87 73
 
1.5%
1 72
 
1.4%
0.86 71
 
1.4%
0.95 69
 
1.4%
Other values (176) 4245
84.9%
ValueCountFrequency (%)
-0.08 1
< 0.1%
-0.07 2
< 0.1%
-0.05 2
< 0.1%
-0.03 2
< 0.1%
-0.02 1
< 0.1%
0 1
< 0.1%
0.01 1
< 0.1%
0.02 1
< 0.1%
0.03 1
< 0.1%
0.05 1
< 0.1%
ValueCountFrequency (%)
2.14 1
< 0.1%
1.9 1
< 0.1%
1.87 1
< 0.1%
1.82 1
< 0.1%
1.81 1
< 0.1%
1.8 2
< 0.1%
1.79 1
< 0.1%
1.78 1
< 0.1%
1.77 1
< 0.1%
1.76 1
< 0.1%

Proximity_to_Industrial_Areas
Real number (ℝ)

Zeros 

Distinct263
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.03188
Minimum0
Maximum46.3
Zeros60
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-12-02T22:35:26.890357image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.295
Q11.5
median3.5
Q36.9
95-th percentile15.105
Maximum46.3
Range46.3
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation5.0103521
Coefficient of variation (CV)0.9957217
Kurtosis5.6367404
Mean5.03188
Median Absolute Deviation (MAD)2.4
Skewness1.9826731
Sum25159.4
Variance25.103628
MonotonicityNot monotonic
2024-12-02T22:35:26.990666image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 98
 
2.0%
0.3 97
 
1.9%
0.1 92
 
1.8%
0.5 89
 
1.8%
0.7 89
 
1.8%
0.8 87
 
1.7%
0.4 83
 
1.7%
0.6 82
 
1.6%
1 82
 
1.6%
1.6 78
 
1.6%
Other values (253) 4123
82.5%
ValueCountFrequency (%)
0 60
1.2%
0.1 92
1.8%
0.2 98
2.0%
0.3 97
1.9%
0.4 83
1.7%
0.5 89
1.8%
0.6 82
1.6%
0.7 89
1.8%
0.8 87
1.7%
0.9 67
1.3%
ValueCountFrequency (%)
46.3 1
< 0.1%
39.7 1
< 0.1%
38.5 1
< 0.1%
38 1
< 0.1%
35.5 1
< 0.1%
34.4 1
< 0.1%
33.2 1
< 0.1%
31.4 1
< 0.1%
29.8 1
< 0.1%
28.7 1
< 0.1%

Population_Density
Real number (ℝ)

Distinct108
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.9482
Minimum243
Maximum358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2024-12-02T22:35:27.087130image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum243
5-th percentile272
Q1288
median300
Q3311
95-th percentile329
Maximum358
Range115
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.215133
Coefficient of variation (CV)0.057393685
Kurtosis-0.099207536
Mean299.9482
Median Absolute Deviation (MAD)12
Skewness0.056287035
Sum1499741
Variance296.36079
MonotonicityNot monotonic
2024-12-02T22:35:27.193841image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
307 124
 
2.5%
293 123
 
2.5%
303 121
 
2.4%
294 121
 
2.4%
296 118
 
2.4%
291 118
 
2.4%
305 116
 
2.3%
302 115
 
2.3%
309 111
 
2.2%
298 109
 
2.2%
Other values (98) 3824
76.5%
ValueCountFrequency (%)
243 1
 
< 0.1%
247 1
 
< 0.1%
249 1
 
< 0.1%
250 2
< 0.1%
251 2
< 0.1%
252 1
 
< 0.1%
253 2
< 0.1%
254 2
< 0.1%
255 2
< 0.1%
256 4
0.1%
ValueCountFrequency (%)
358 1
 
< 0.1%
357 3
0.1%
356 1
 
< 0.1%
355 1
 
< 0.1%
353 1
 
< 0.1%
352 3
0.1%
350 1
 
< 0.1%
349 2
< 0.1%
346 3
0.1%
345 3
0.1%

Air Quality
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size306.3 KiB
Good
2000 
Moderate
1500 
Poor
1000 
Hazardous
500 

Length

Max length9
Median length4
Mean length5.7
Min length4

Characters and Unicode

Total characters28500
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHazardous
2nd rowGood
3rd rowGood
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Good 2000
40.0%
Moderate 1500
30.0%
Poor 1000
20.0%
Hazardous 500
 
10.0%

Length

2024-12-02T22:35:27.296196image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T22:35:27.377553image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
good 2000
40.0%
moderate 1500
30.0%
poor 1000
20.0%
hazardous 500
 
10.0%

Most occurring characters

ValueCountFrequency (%)
o 8000
28.1%
d 4000
14.0%
e 3000
 
10.5%
r 3000
 
10.5%
a 2500
 
8.8%
G 2000
 
7.0%
M 1500
 
5.3%
t 1500
 
5.3%
P 1000
 
3.5%
H 500
 
1.8%
Other values (3) 1500
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23500
82.5%
Uppercase Letter 5000
 
17.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8000
34.0%
d 4000
17.0%
e 3000
 
12.8%
r 3000
 
12.8%
a 2500
 
10.6%
t 1500
 
6.4%
z 500
 
2.1%
u 500
 
2.1%
s 500
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
G 2000
40.0%
M 1500
30.0%
P 1000
20.0%
H 500
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8000
28.1%
d 4000
14.0%
e 3000
 
10.5%
r 3000
 
10.5%
a 2500
 
8.8%
G 2000
 
7.0%
M 1500
 
5.3%
t 1500
 
5.3%
P 1000
 
3.5%
H 500
 
1.8%
Other values (3) 1500
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8000
28.1%
d 4000
14.0%
e 3000
 
10.5%
r 3000
 
10.5%
a 2500
 
8.8%
G 2000
 
7.0%
M 1500
 
5.3%
t 1500
 
5.3%
P 1000
 
3.5%
H 500
 
1.8%
Other values (3) 1500
 
5.3%

Interactions

2024-12-02T22:35:24.360258image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.335107image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.037020image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.641669image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.236055image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.850719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.444837image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.183798image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.762685image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.425850image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.396261image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.098765image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.705168image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.297386image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.913641image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.505250image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.243398image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.825565image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.495317image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.458606image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.160988image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.770740image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.363908image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.983351image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.568621image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.305228image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.891315image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.567722image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.522856image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.225106image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.836713image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.430875image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.050935image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.632545image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.369058image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.959462image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.635837image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.587504image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.289707image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.904809image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.497769image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.119306image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.697556image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.436141image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.027714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.706862image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.652295image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.361543image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.972468image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.564724image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.184151image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.763417image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.502916image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.093541image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.775800image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.714614image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.433217image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.034328image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.634594image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.247715image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.989162image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.566553image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.157263image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.844464image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.904105image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.509292image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.099471image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.706606image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.309872image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.051205image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.627788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.221287image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.915403image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:19.968232image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:20.573710image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.164575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:21.776445image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:22.375608image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.114569image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:23.692120image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-02T22:35:24.287309image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-02T22:35:27.439230image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Air QualityCOHumidityNO2PM10PM2.5Population_DensityProximity_to_Industrial_AreasSO2Temperature
Air Quality1.0000.0140.0000.0090.0000.0000.0000.0000.0060.004
CO0.0141.000-0.0180.013-0.0030.005-0.015-0.0240.0140.018
Humidity0.000-0.0181.0000.003-0.004-0.010-0.0100.002-0.023-0.017
NO20.0090.0130.0031.000-0.0030.000-0.028-0.0030.005-0.022
PM100.000-0.003-0.004-0.0031.0000.960-0.006-0.023-0.032-0.010
PM2.50.0000.005-0.0100.0000.9601.000-0.006-0.020-0.026-0.001
Population_Density0.000-0.015-0.010-0.028-0.006-0.0061.000-0.0100.0010.017
Proximity_to_Industrial_Areas0.000-0.0240.002-0.003-0.023-0.020-0.0101.000-0.0100.001
SO20.0060.014-0.0230.005-0.032-0.0260.001-0.0101.0000.013
Temperature0.0040.018-0.017-0.022-0.010-0.0010.0170.0010.0131.000

Missing values

2024-12-02T22:35:25.014781image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-02T22:35:25.138277image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TemperatureHumidityPM2.5PM10NO2SO2COProximity_to_Industrial_AreasPopulation_DensityAir Quality
027.251.735.146.226.732.20.9811.2314Hazardous
126.359.31.06.238.320.40.6813.5298Good
227.973.220.039.419.65.80.955.4309Good
323.951.914.724.35.212.61.244.5282Poor
425.259.026.330.926.813.51.065.6293Poor
522.761.44.33.211.121.00.595.6290Good
631.267.949.662.426.214.31.474.0313Good
725.156.936.858.720.420.40.820.4300Poor
835.775.4101.7115.436.92.11.032.4295Moderate
924.361.60.215.126.117.00.641.7311Moderate
TemperatureHumidityPM2.5PM10NO2SO2COProximity_to_Industrial_AreasPopulation_DensityAir Quality
499028.545.68.923.97.76.20.500.4301Good
499132.677.523.332.313.311.81.178.5311Good
499218.587.123.831.623.18.01.026.6275Good
499322.563.2100.4118.133.47.90.531.6310Poor
499427.250.665.573.511.524.90.966.0285Good
499529.336.880.390.99.214.10.9710.2287Moderate
499615.751.70.711.440.513.81.074.2320Good
499727.848.18.916.48.617.70.540.3302Moderate
499830.450.42.218.813.122.30.946.7308Moderate
499921.576.545.058.037.90.00.960.2290Hazardous